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利用 GNSS 反演的天顶延迟和 PWV 对中国中南部 PM 浓度进行空间插值。

Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM Concentration in Central and Southern China.

机构信息

College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.

Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541006, China.

出版信息

Int J Environ Res Public Health. 2021 Jul 27;18(15):7931. doi: 10.3390/ijerph18157931.

Abstract

With the increasing application of global navigation satellite system (GNSS) technology in the field of meteorology, satellite-derived zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data have been used to explore the spatial coverage pattern of PM concentrations. In this study, the PM concentration data obtained from 340 PM ground stations in south-central China were used to analyze the variation patterns of PM in south-central China at different time periods, and six PM interpolation models were developed in the region. The spatial and temporal PM variation patterns in central and southern China were analyzed from the perspectives of time series variations and spatial distribution characteristics, and six types of interpolation models were established in central and southern China. (1) Through correlation analysis, and exploratory regression and geographical detector methods, the correlation analysis of PM-related variables showed that the GNSS-derived PWV and ZTD were negatively correlated with PM, and that their significances and contributions to the spatial analysis were good. (2) Three types of suitable variable combinations were selected for modeling through a collinearity diagnosis, and six types of models (geographically weighted regression (GWR), geographically weighted regression kriging (GWRK), geographically weighted regression-empirical bayesian kriging (GWR-EBK), multiscale geographically weighted regression (MGWR), multiscale geographically weighted regression kriging (MGWRK), and multiscale geographically weighted regression-empirical bayesian kriging (MGWR-EBK)) were constructed. The overall of the GWR-EBK model construction was the best (annual: 0.962, winter: 0.966, spring: 0.926, summer: 0.873, and autumn: 0.908), and the interpolation accuracy of the GWR-EBK model constructed by inputting ZTD was the best overall, with an average RMSE of 3.22 μg/m recorded, while the GWR-EBK model constructed by inputting PWV had the highest interpolation accuracy in winter, with an RMSE of 4.5 μg/m recorded; these values were 2.17% and 4.26% higher than the RMSE values of the other two types of models (ZTD and temperature) in winter, respectively. (3) The introduction of the empirical Bayesian kriging method to interpolate the residuals of the models (GWR and MGWR) and to then correct the original interpolation results of the models was the most effective, and the accuracy improvement percentage was better than that of the ordinary kriging method. The average improvement ratios of the GWRK and GWR-EBK models compared with that of the GWR model were 5.04% and 14.74%, respectively, and the average improvement ratios of the MGWRK and MGWR-EBK models compared with that of the MGWR model were 2.79% and 12.66%, respectively. (4) Elevation intervals and provinces were classified, and the influence of the elevation and the spatial distribution of the plane on the accuracy of the PM regional model was discussed. The experiments showed that the accuracy of the constructed regional model decreased as the elevation increased. The accuracies of the models in representing Henan, Hubei and Hunan provinces were lower than those of the models in representing Guangdong and Guangxi provinces.

摘要

随着全球导航卫星系统(GNSS)技术在气象领域的应用不断增加,卫星反演的天顶对流层延迟(ZTD)和可降水量(PWV)数据已被用于探索 PM 浓度的空间覆盖模式。本研究使用了来自中国中南部 340 个 PM 地面站的 PM 浓度数据,分析了中国中南部不同时间段的 PM 变化模式,并在该区域开发了六种 PM 插值模型。从中南部 PM 浓度的时空变化格局出发,采用时间序列变化和空间分布特征分析,建立了中南部 6 种插值模型。(1)通过相关性分析、探索性回归和地理探测器方法,对 PM 相关变量的相关性分析表明,GNSS 反演的 PWV 和 ZTD 与 PM 呈负相关,其对空间分析的显著性和贡献度良好。(2)通过共线性诊断选择了三种合适的变量组合进行建模,并构建了六种模型(地理加权回归(GWR)、地理加权回归克里金(GWRK)、地理加权回归-经验贝叶斯克里金(GWR-EBK)、多尺度地理加权回归(MGWR)、多尺度地理加权回归克里金(MGWRK)和多尺度地理加权回归-经验贝叶斯克里金(MGWR-EBK))。GWR-EBK 模型构建的整体效果最好(年:0.962,冬季:0.966,春季:0.926,夏季:0.873,秋季:0.908),输入 ZTD 构建的 GWR-EBK 模型插值精度整体最好,平均 RMSE 为 3.22μg/m,输入 PWV 构建的 GWR-EBK 模型冬季插值精度最高,RMSE 为 4.5μg/m,比冬季其他两种模型(PWV 和温度)的 RMSE 分别高出 2.17%和 4.26%。(3)引入经验贝叶斯克里金方法对模型(GWR 和 MGWR)的残差进行插值,然后对模型的原始插值结果进行修正,是最有效的方法,精度提高的百分比优于普通克里金方法。GWRK 和 GWR-EBK 模型与 GWR 模型相比,平均改进率分别为 5.04%和 14.74%,MGWRK 和 MGWR-EBK 模型与 MGWR 模型相比,平均改进率分别为 2.79%和 12.66%。(4)对高程区间和省份进行分类,讨论了高程和平面空间分布对 PM 区域模型精度的影响。实验表明,随着高程的增加,构建的区域模型的精度降低。在代表河南、湖北和湖南三省的模型中,其精度低于在代表广东和广西两省的模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6909/8345597/923d8e34ad5f/ijerph-18-07931-g001.jpg

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